1. Field of the Invention
Embodiments of the present invention relate to methods of improving the accuracy of flight management systems based on mathematical models of aircraft performance. In particular, the present invention pertains to a method of improving mathematical prediction models, which are calculated from aircraft specific data, by adding engine sensor data to the calculations, checking captured sensor and pilot entered data, and comparing data measured from redundant sensors.
2. Background of the Invention
Early methods of predicting aircraft performance characteristics were often based on “rules of thumb” in which a pilot, based on previous experience, makes a reasonable estimate of the aircraft performance characteristics. More recent aircraft performance prediction systems include a custom performance computer that utilizes “average parameters” for a predetermined model of an aircraft. While this computer-based system has significant advantages over the older “rules of thumb” method, the computer-based average parameter method is useful for only one aircraft type, and must be repeated for each new type of aircraft. The foregoing methods and systems have other inaccuracies and deficiencies. For example, the “rules of thumb” method is very inaccurate since it is difficult to compensate for temperature of the air, weight of the aircraft, etc. Also, this method usually adds to the pilot workload at a time when the pilot is already very busy. The custom performance computer method, because of the average parameter disadvantage, does not account for manufacturing tolerances, optional equipment, a degradation of parameters resulting from age and usage, nor for different pilot techniques.
U.S. Pat. No. 5,070,458 to Gilmore, et al., entitled “Method of Analyzing and Predicting both Airplane and Engine Performance Characteristics,” issued Dec. 3, 1991, describes a method that overcomes many of the disadvantages of the prediction methods described above. The method described by Gilmore et al. makes performance predictions for an individual aircraft and engine using parameters that are learned from “flight to flight.” The parameters are stored as data in a system database for subsequent recall, are initialized with reasonable values of specific performance parameters, and are adjusted or updated with new performance parameters resulting from each flight of the aircraft. Thus, the performance characteristics of a given aircraft are learned from flight to flight for use in future flights of the given aircraft. The learned parameter technique thereby adjusts to changes in the aircraft due to aging, is tailored to a specific aircraft, and accounts for manufacturing tolerances. However, in Gilmore, et al., the modeling utilized and the computation of terms thereof are inefficient. In addition, Gilmore, et al. use a model that separates thrust and drag terms of the model when making predictions and is somewhat limited in the number of outputs produced. For example, Gilmore et al. do not provide for predictions of long range cruise speed or optimum altitude, which are important parameters for achieving the best possible fuel efficiency.
U.S. Pat. No. 5,606,505 to Smith, et al., entitled “Method of Airplane Performance Estimation and Prediction,” issued Feb. 25, 1997, describes, in turn, a method that overcomes many of the disadvantages of Gilmore et al. This method produces an enhanced set of outputs which are provided with greater speed and/or accuracy. The estimating method used to predict performance characteristics in Smith et al. includes modeling the aircraft with at least one mathematical model using aircraft specific data and input parameters that define the performance characteristics. Coefficients of the mathematical model are learned during at least one flight, and climb and cruise performance characteristics are predicted as a function of the learned coefficients of the mathematical model. Performance characteristics predicted by this method include long range cruise speed, maximum cruise speed, optimum altitude at a certain cruise speed, maximum altitude at a certain cruise speed, the location of the top of climb, and total fuel burned for a given flight plan. One specific method of modeling the aircraft, described in Smith et al., is modeling the thrust-minus-drag of the aircraft. The predicted performance characteristics are then a function only of the non-independent or combination thrust-minus-drag relationship, and not of thrust and drag independently.
However, the modeling method of Smith et al. based on the thrust-minus-drag of the aircraft has at least one limitation. In a cruise condition, current acceleration is zero and the thrust-minus-drag equation will, therefore, equal zero. As a result, it is difficult to identify model parameters in a cruise condition. In practice, this effect has been observed as parameter “drift”. In other words, the parameter estimates will wander, or predictions will be off if the aircraft spends a long time in cruise. Also, due to model mismatch, the observation matrix of the Kalman filter, described in Smith et al., is often full rank, which will tend to drive the parameter estimates to a zero solution. In view of the foregoing, it can be appreciated that a substantial need exists for a method of accurately calculating prediction parameters using a thrust-minus-drag model for all stages of flight, including a level, constant-speed cruise condition.
Another limitation of the method of Smith et al. is its total dependency on the accuracy of sensory or pilot entered data. If a sensor such as the fuel flow sensor fails, and this is not detected, parameter identification errors will result. Similarly, if a pilot enters erroneous data, such as the wrong initial weight of the aircraft, parameter identification errors will also occur. In view of the foregoing, it can be appreciated that a substantial need exists for improving the accuracy of estimated prediction parameters.
Finally, the method of Smith et al. does not utilize the redundant sensors and systems on aircraft. Generally, there are at least two of each of the types of sensors feeding data to the algorithm of Smith et al. For instance, there are typically two air data computers on an aircraft: one on the right side and one on the left. These computers collect two independent sets of the same data. Currently, the algorithm of Smith et al. is run on one set of this data. In view of the foregoing, it can be appreciated that a substantial need exists for improving the accuracy of estimated prediction parameters.